//- 💫 DOCS > USAGE > SPACY 101 > WORD VECTORS

p
    |  Similarity is determined by comparing #[strong word vectors] or "word
    |  embeddings", multi-dimensional meaning representations of a word. Word
    |  vectors can be generated using an algorithm like
    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec] and usually
    |  look like this:

+code("banana.vector", false, false, 250).
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          -1.19659996e+00,  -4.71559986e-02,   5.31750023e-01], dtype=float32)

+infobox("Important note", "⚠️")
    |  To make them compact and fast, spaCy's small #[+a("/models") models]
    |  (all packages that end in #[code sm]) #[strong don&apos;t ship with word vectors], and
    |  only include context-sensitive #[strong tensors]. This means you can
    |  still use the #[code similarity()] methods to compare documents, spans
    |  and tokens – but the result won't be as good, and individual tokens won't
    |  have any vectors assigned. So in order to use #[em real] word vectors,
    |  you need to download a larger model:

    +code-wrapper
        +code-new(false, "bash", "$") python -m spacy download en_core_web_lg

p
    |  Models that come with built-in word vectors make them available as the
    |  #[+api("token#vector") #[code Token.vector]] attribute.
    |  #[+api("doc#vector") #[code Doc.vector]] and
    |  #[+api("span#vector") #[code Span.vector]] will default to an average
    |  of their token vectors. You can also check if a token has a vector
    |  assigned, and get the L2 norm, which can be used to normalise
    |  vectors.

+code.
    nlp = spacy.load('en_core_web_lg')
    tokens = nlp(u'dog cat banana sasquatch')

    for token in tokens:
        print(token.text, token.has_vector, token.vector_norm, token.is_oov)

+aside
    |  #[strong Text]: The original token text.#[br]
    |  #[strong has vector]: Does the token have a vector representation?#[br]
    |  #[strong Vector norm]: The L2 norm of the token's vector (the square root
    |  of the sum of the values squared)#[br]
    |  #[strong is OOV]: Is the word out-of-vocabulary?

+table(["Text", "Has vector", "Vector norm", "OOV"])
    - var style = [0, 1, 1, 1]
    +annotation-row(["dog", true, 7.033672992262838, false], style)
    +annotation-row(["cat", true, 6.68081871208896, false], style)
    +annotation-row(["banana", true, 6.700014292148571, false], style)
    +annotation-row(["sasquatch", false, 0, true], style)

p
    |  The words "dog", "cat" and "banana" are all pretty common in English, so
    |  they're part of the model's vocabulary, and come with a vector. The word
    |  "sasquatch" on the other hand is a lot less common and out-of-vocabulary
    |  – so its vector representation consists of 300 dimensions of #[code 0],
    |  which means it's practically nonexistent. If your application will
    |  benefit from a #[strong large vocabulary] with more vectors, you should
    |  consider using one of the larger models or loading in a full vector
    |  package, for example,
    |  #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]], which
    |  includes over #[strong 1 million unique vectors].
